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A note on the polynomial ergodicity of the one-dimensional Zig-Zag process

Published online by Cambridge University Press:  18 July 2022

Giorgos Vasdekis*
Affiliation:
University of Warwick
Gareth O. Roberts*
Affiliation:
University of Warwick
*
*Postal address: Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK.
*Postal address: Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK.
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Abstract

We prove polynomial ergodicity for the one-dimensional Zig-Zag process on heavy-tailed targets and identify the exact order of polynomial convergence of the process when targeting Student distributions.

Information

Type
Original Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Applied Probability Trust
Figure 0

Figure 1. Mean square error over time of three Zig-Zag algorithms with different refresh rates $\gamma(x)$, described in the upper right corner. The algorithms target the Cauchy distribution and are used to estimate the probability mass the Cauchy distribution assigns to the event $[5,+\infty)$. For all three algorithms, 1000 independent realisations were generated, and for each time less than $10^4$, the average square error between the approximation of the probability and the actual probability (approximately $0.0628$) is reported. Evidently the fastest convergence is achieved by lowering the values of $\gamma$.